Cat Swarm Optimization: Theory ,Practices & Applications

Cat Swarm Optimization adopts a swarm of cats to represent the potential solutions of an optimization problem. In 2006, by analyzing the behavior of Cats, Shu-Chuan Chu, Pei-Wei Tsai, and Jeng-Shyang Pan, developed CSO algorithm.CSO performs learning and multi-modal search through two processes, namely seeking mode and tracing mode. Cat Swarm optimization provides scalability and adaptability to varied real-time applications. The CSO algorithm is extremely parallel and presents a fine tractability in terms of computational time and cost. The research fraternity had utilized, modified, enhanced the original CSO algorithm for solving many real time problems. In spite of multitude of publications in CSO till date, nearly around 300 articles are available as per the Google Scholar Reports in 2019. The goal is to improve the already existing solutions, refine them, and define them as per the real time applications. This book includes the background information on CSO, variants of CSO, and its applications in varied disciplines. The book is intended for graduate students, professors, researchers, and industry professionals interested in optimization techniques.

Autorentext

Dr.M.Dhivya is working as Associate Professor in the Department of ECE at New Horizon College of Engineering, Bangalore,India. Her research Interests are Wireless Sensor Networks, Embedded & Real Time Systems, Optimization Techniques and Machine Learning. Academia Profile: https://annauniv.academia.edu/ DhivyaManian.